Image recognition is an important field of development today, having become more so in the last few years for the purposes of homeland security. Among the tasks that need to be performed in image recognition is the task of character recognition. One significant aspect of character recognition is attempting to perform character recognition when the characters are surrounded at least partially by clutter. Two examples of this clutter aspect of character recognition that are clearly within the area of security are license plate recognition and vehicle signage recognition (that is, signs on the backs or sides of trucks and vans). In both of these examples the important characters may be presented with clutter around the characters. For license plates, the clutter could be artwork at the bottom of the license plate that extends into the characters. For truck signage, artwork could also extend into the characters. In either example, dirt could clutter the characters. Other examples may not be closely related to security but may have economic importance.
In addition to security value, license plate recognition has commercial value for tollway enforcement. Recognition of characters on printed materials such as magazine covers or T shirts could be useful for commercial purposes. One example is television coverage within which persons or objects are best presented with anonymity (an example is reality police shows), so it may be important to identify characters on license plates or T-shirts that need to be obscured before or while being broadcast. The characters may be cluttered with artwork.
Present image processing techniques have employed refinements such as pixel projection of a binarized image that is suspected to include at least a row of characters to attempt to improve character recognition, but there is substantial room for improved reliability before such techniques even approach the ability of a person to achieve character recognition under circumstances of clutter. The binarized images used in these present image processing techniques may be generated from a gray scale image using generalized threshold or edge determination techniques. These present image processing techniques suffer in their reliability of determining individual characters, particularly when the image includes background clutter within the characters, either at their top or bottom regions, or in some combination.
Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of embodiments of the present invention.